Effect of spatial resolution on classification errors of pure and mixed pixels in remote sensing

It is observed in remote sensing that a finer spatial resolution does not necessarily improve the classification performance. These observations have been understood by using the conceptual explanation that "boundary effect" and "within-class variability" work against one another. Though easily understood, this conceptual explanation cannot be readily used for a quantitative investigation. The authors design a simulation scheme to evaluate systematically the impacts of various parameters on the classification accuracy. The authors employ a model for the class spectral covariance of pure pixels and a linear mixing model for the spectral responses of mixed pixels. Based on these models, the authors derive the statistical characteristics for mixed pixels and assess the corresponding classification errors. As the ratio of ground sampling distance to field size decreases, the classification error associated with pure pixels tends to increase, whereas the classification error associated with mixed pixels tends to decrease from the smaller area of mixed pixels. The simulation results show that the overall classification error first decreases with decreasing ratio of ground sampling distance to field width, reaches a minimum value, and then may increase with further decreasing ratio. The study on the classification error may help the development of classification schemes for high spatial resolution imagery.